IAM

DAVIDSTUTZ

ARCHIVEMONTHLY»SEPTEMBER2019«

09thSEPTEMBER2019

READING NOTES

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin. Black-box Adversarial Attacks with Limited Queries and Information. ICML, 2018.
ADVERSARIAL MACHINE LEARNING DEEP LEARNING

More ...

07thSEPTEMBER2019

READING NOTES

Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli. On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks. CoRR abs/1809.02861 (2018).
ADVERSARIAL MACHINE LEARNING DEEP LEARNING

More ...

05thSEPTEMBER2019

READING NOTES

Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang. Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples. NeurIPS, 2018.
ADVERSARIAL MACHINE LEARNING DEEP LEARNING

More ...

04thSEPTEMBER2019

READING NOTES

Sungrae Park, Jun-Keon Park, Su-Jin Shin, Il-Chul Moon. Adversarial Dropout for Supervised and Semi-Supervised Learning. AAAI, 2018.

More ...

02ndSEPTEMBER2019

READING NOTES

Kang Liu, Brendan Dolan-Gavitt, Siddharth Garg. Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. CoRR abs/1805.12185 (2018).
ADVERSARIAL MACHINE LEARNING DEEP LEARNING

More ...

01stSEPTEMBER2019

READING NOTES

Marc Khoury, Dylan Hadfield-Menell. On the Geometry of Adversarial Examples. CoRR abs/1811.00525 (2018).
ADVERSARIAL MACHINE LEARNING DEEP LEARNING

More ...

SEARCHTHEBLOG

ARCHIVES

TAGS

COPYRIGHT DAVIDSTUTZ IMPRESSUM & DATENSCHUTZ